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RESEARCH PAPER · npj Computational Materials · 2017

Virtual screening of inorganic materials synthesis parameters with deep learning

Edward Kim, Kejun Huang, Stefanie Jegelka, Elsa Olivetti

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Virtual screening of inorganic materials synthesis parameters with deep learningOriginalUnderstanding structural adaptability: a reactant informatics approach to experiment design12Data mining for better material synthesis: The case of pulsed laser deposition of complex oxides38A strategy to apply machine learning to small datasets in materials science776Toward a sustainable materials system257Using deep neural network with small dataset to predict material defects512Empirical modeling of dopability in diamond-like semiconductors46Nanoinformatics, and the big challenges for the science of small things92Strategies for improving the sustainability of structural metals724Machine learning based prediction of metal hydrides for hydrogen storage, part I: Prediction of hydrogen weight percent135Transfer-Learning-Based Coarse-Graining Method for Simple Fluids: Toward Deep Inverse Liquid-State Theory47Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora16Efficient construction method for phase diagrams using uncertainty sampling61A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules113New frontiers for the materials genome initiative562A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction282Machine learning for hierarchical prediction of elastic properties in Fe-Cr-Al system28Mechanistically Driven Control over Cubane Oxo Cluster Catalysts29General Atomic Neighborhood Fingerprint for Machine Learning-Based Methods49Deep Learning in Chemistry561The machine learning revolution in materials?69Semi-supervised machine-learning classification of materials synthesis procedures150Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature307Inverse Structural Design of Graphene/Boron Nitride Hybrids by Regressional GAN0Combining large-scale screening and machine learning to predict the metal-organic frameworks for organosulfurs removal from high-sour natural gas33Methods for Open and Reproducible Materials Science2Discovery of blue singlet exciton fission molecules via a high-throughput virtual screening and experimental approach42A picture is worth a thousand words: applying natural language processing tools for creating a quantum materials database map17Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonomous Discovery in the Chemical Sciences Part I: Progress317A multi-fidelity information-fusion approach to machine learn and predict polymer bandgap102MATGANIP: Learning to Discover the Structure-Property Relationship in Perovskites with Generative Adversarial Networks5Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning65Representing Multiword Chemical Terms through Phrase-Level Preprocessing and Word Embedding13ImageDataExtractor: A Tool To Extract and Quantify Data from Microscopy Images58Artificial Intelligence-Based New Material Design1Recommender System of Successful Processing Conditions for New Compounds Based on a Parallel Experimental Data Set16Machine Learning Approaches for Thermoelectric Materials Research231Deep-learning-based quality filtering of mechanically exfoliated 2D crystals78Electron configuration-based neural network model to predict physicochemical properties of inorganic compounds7Information Extraction and Graph Representation for the Design of Formulated Products7Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity123Prediction of optoelectronic properties of Cu 2 O using neural network potential3Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks142Design space visualization for guiding investments in biodegradable and sustainably sourced materials7Materials informatics approach to understand aluminum alloys24Materials Informatics Screening of Li‐Rich Layered Oxide Cathode Materials with Enhanced Characteristics Using Synthesis Data29Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network5A Design-to-Device Pipeline for Data-Driven Materials Discovery114Perovskite Oxides Prepared by Hydrothermal and Solvothermal Synthesis: A Review of Crystallisation, Chemistry, and Compositions152Machine learning in materials genome initiative: A review231Microfluidic Synthesis of Luminescent and Plasmonic Nanoparticles: Fast, Efficient, and Data‐Rich87Models for the solubility calculation of a CO2/polymer system: A review15Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning7Automated extraction of chemical synthesis actions from experimental procedures202Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN61AI Applications through the Whole Life Cycle of Material Discovery192Machine-learned metrics for predicting the likelihood of success in materials discovery38Predictive Synthesis of Quantum Materials by Probabilistic Reinforcement Learning0Practicing deep learning in materials science: An evaluation for predicting the formation energies17Deep-Learning Architecture in QSPR Modeling for the Prediction of Energy Conversion Efficiency of Solar Cells18MultiCon: A Semi-Supervised Approach for Predicting Drug Function from Chemical Structure Analysis29Emerging materials intelligence ecosystems propelled by machine learning441State Primitive Learning to Overcome Catastrophic Forgetting in Robotics6Text mining for processing conditions of solid-state battery electrolytes75Data-driven materials research enabled by natural language processing and information extraction302Ionic liquid promoted extraction of bitumen from oil sand: A review21Deep learning for material synthesis and manufacturing systems: A review74DeepReac+: deep active learning for quantitative modeling of organic chemical reactions52Experimental design for the highly accurate prediction of material properties using descriptors obtained by measurement9Far-from-equilibrium effects of electric and electromagnetic fields in ceramics synthesis and processing19Finely tuned inverse design of metal–organic frameworks with user-desired Xe/Kr selectivity40Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review44Self-assembled nickel cubanes as oxygen evolution catalysts17Thermodynamic exploration of xenon/krypton separation based on a high-throughput screening16Toward autonomous design and synthesis of novel inorganic materials159Manufacturing scalability implications of materials choice in inorganic solid-state batteries65Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21 st century55Opportunities and challenges of text mining in materials research180Machine learning for material characterization with an application for predicting mechanical properties139Machine learning approaches for permittivity prediction and rational design of microwave dielectric ceramics80Data Science in Chemical Engineering: Applications to Molecular Science20EXSCLAIM! -- An automated pipeline for the construction of labeled materials imaging datasets from literature9Analytical gradients for molecular-orbital-based machine learning13Accelerating two-dimensional X-ray diffraction measurement and analysis with density-based clustering for thin films4Integrated deep learning neural network and desirability analysis in biogas plants: A powerful tool to optimize biogas purification27Looking through glass: Knowledge discovery from materials science literature using natural language processing60Predictive Synthesis54Nanoparticle synthesis assisted by machine learning545Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials45Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354An exploratory analysis: extracting materials science knowledge from unstructured scholarly data4Virtual experimentations by deep learning on tangible materials25Synthesis‐condition recommender system discovers novel inorganic oxides2Toward Predictive Chemical Deformulation Enabled by Deep Generative Neural Networks7The latest process and challenges of microwave dielectric ceramics based on pseudo phase diagrams211Data Science Applied to Carbon Materials: Synthesis, Characterization, and Applications5Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks198Dataset of Solution-based Inorganic Materials Synthesis Recipes Extracted from the Scientific Literature2An understanding of hydrogen embrittlement in nickel grain boundaries from first principles69Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning8Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery98Application of Machine Learning in Determining the Mechanical Properties of Materials22Discovery Acceleration vs Epistemic Depth: Speed–Understanding Trade-Offs in Computational Design0High-throughput computational screening of nanoporous materials in targeted applications65Property Prediction vs Mechanistic Insight: A Conceptual Divide in Materials AI0ULSA: unified language of synthesis actions for the representation of inorganic synthesis protocols28Analysis of Neural Machine Translation KANGRI Language by Unsupervised and Semi Supervised Methods10High-efficient low-cost characterization of materials properties using domain-knowledge-guided self-supervised learning0Automated pipeline for superalloy data by text mining83Extended DeepILST for Various Thermodynamic States and Applications in Coarse-Graining9Deep learning modeling strategy for material science: from natural materials to metamaterials29Inverse Design of Materials by Machine Learning137Artificial intelligence to bring nanomedicine to life189MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks135Machine Learning for the Discovery, Design, and Engineering of Materials66Text Mining for Energy Materials3Recent advances and applications of deep learning methods in materials science1161Data‐Driven Materials Innovation and Applications187Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures21Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature83Materials information extraction via automatically generated corpus30Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space18An automatic descriptors recognizer customized for materials science literature36Developing Catalysts via Structure‐Property Relations Discovered by Machine Learning: An Industrial Perspective6Prediction of hydrogen storage in dibenzyltoluene empowered with machine learning38Hydrogen Storage Prediction in Dibenzyltoluene as Liquid Organic Hydrogen Carrier Empowered with Weighted Federated Machine Learning26Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Deep Learning in Diverse Intelligent Sensor Based Systems65Artificial Intelligence and Advanced Materials118Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction41Prediction of lattice thermal conductivity with two-stage interpretable machine learning17Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning34Review: 2D material property characterizations by machine-learning-assisted microscopies16Small data machine learning in materials science866Exploring the Possibility of Machine Learning for Predicting Ionic Conductivity of Solid-State Electrolytes54Computational Discovery of Stable Metal–Organic Frameworks for Methane-to-Methanol Catalysis34EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets16Alloy synthesis and processing by semi-supervised text mining28Computational materials discovery and development for Li and non-Li advanced battery chemistries8Prediction of hydrogen generation from perhydro-dibenzyltoluene empowered with machine learning6Opinion Mining by Convolutional Neural Networks for Maximizing Discoverability of Nanomaterials8Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis68Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation15Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO312Natural Language Processing7Property Prediction0Novelty Detection in the Design of Synthesis of Energy Storage Materials: a Case Study of Garnet-Structured Solid Electrolytes1MatKG: An autonomously generated knowledge graph in Material Science86Supervised Machine Learning-Based Prediction of Hydrogen Storage Classes Utilizing Dibenzyltoluene as an Organic Carrier16Novelty detection in the design of synthesis of garnet-structured solid electrolytes6

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